HusseinLezzaik

What happens in a ML Interview?

• Background and culture fit
• Whiteboard Coding (similar to SWE interviews)
• Pair Coding (similar to SWE-interviews)
• Pair debugging (often ML-specific code)
• Math puzzles (e.g. involving Linear Algebra)
• Take-home ML project
• Applied ML (e.g. explain how you'd solve this problem with ML)
• Previous ML projects (e.g. probing on what you tried, why things did/didn't work)
• ML Theory (e.g. bias-variance tradeoff, overfitting, underfitting, understanding of specific algorithms)

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Strategy

• Gather System Requirements
• Plan
• Estimate
• Communicate
• Diagram

Estimation Cheatsheet

Units

• 1 kB = 1000 bytes

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Hacker Blogs

• How to Become a Hacker **
• The Metamorphosis of Prime Intellect
• AN OPEN LETTER TO OPEN-MINDED PROGRESSIVES
• How To Drop Out

HusseinLezzaik

ML-Fundamentals

• ConvNetJS, DeepLearning in Your Browser
• TensorFlow Examples
• Code for Hands-on ML using Scikit-Learn, Tensorflow, and Keras
• TensorFlow Tutorials
• Code for ML with PyTorch and Sci-kit Learn
• Large Model Parallelism Notebook
• Datascience IPython Notebooks
• Code for Artificial Intelligence, A Modern Approach
• Christopher Olah's Blog

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Blogs

• Why Software is eating the world, Marc Andreesen
• Reinventing Societal Infrastructure with Technology, Vinod Khosla
• Hard Startups, Sam Altman
• 5 Step Organization Process, Elon Musk
• Part I: Founder-Led Enterprise Sales, Zero to $5M in ARR
• Part II: The failure points from $5m to $100m in ARR
• 2020 Letter to Shareholders, Jeff Bezos
• [High Technology Innovation, Free Markets or Government Subsidies, T. J. Rodgers](https://tjrodgers.com/news/dr-rodgers-testimony-high-technology-innovation-free-mar

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List of Tools

• List of Tools for MLOps - Chip Huyen
• LF AI & Data Foundation Interactive Landscape
• The AI and Data Landscape
• Data50: The Worlds Top Data Startups
• My MLOps Stack

Resources

• Production Level Deep Learning
• ApplyingML

HusseinLezzaik

Blogs

• The Four Innovation Phases of Netflix’s Trillions Scale Real-time Data Infrastructure
• Machine Learning Production Myths, Chip Huyen
• Three Principles for Designing ML-Powered Products
• Continuous Delivery for Machine Learning
• Best Practices for ML Engineering - Google
• 150 successful ml models: 6 lessons learned at Booking.com
• Machine Learning How To: Reproducible and Trackable Training
• [Data Drift Detection for Large Datasets - Evidently ai](https://evidentlyai.com/blog/d

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Linear Algebra

• Stanford CS229 Linear Algebra Review
• The Matrix Cookbook
• Linear algebra cheat sheet for deep learning - Towards DataScience

Probability & Statistics

• Probability Cheatsheet v2.0
• 109 Data Science Interview Questions and Answers
• 40 Interview Questions On Statistics For Data Scientists
• Prob and Statistics CookBook

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# Vanilla Crew Scheduling

# Step 1: Define the flights and crew members, along with the flight times and max work hours.

# Flight times in hours
flight_times = {
    "flight_1": 5,
    "flight_2": 3,
    "flight_3": 4,
}

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## Pip
```python
$ pip install package_name==required_version
$ pip uninstall package_name
```

## Install via Brew
```python
$ brew install python@3.10
```